Recent advances in the field of developmental neuroscience have revealed many of the mechanisms controlling proliferation, patterning, neurogenesis, fate determination, migration, differentiation, pathway navigation, synaptogenesis and cell death. What has been lacking, however, is an understanding of the mechanisms that control neuronal positioning: what are the determinants of neuronal position within a brain structure, and how is a cell related to the positioning of other cells of the same or different type? The present exploratory proposal will create software tools for describing the geometrical relationship between neurons in a volume of tissue, and for modeling their positioning in three-dimensional space. Drawing upon such tools that have been successfully employed to describe the geometry and spatial relationships between retinal neurons distributed in two dimensions, we will extend such Matlab-based scripts to enable the same sorts of analyses in three dimensions. X-Y-Z positional information will be extracted from samples of brain tissue labeled to reveal individual types of neuron and/or glial cell, upon which a variety of Voronoi domain-based computations will be performed, including the measurement of Voronoi domain volumes, Delaunay segment lengths, nearest neighbor distances, and Voronoi facet areas. Auto-correlation analysis will be performed to identify whether there is any consistent higher-order patterning in the relationship between cells of a given type, or any evidence of exclusion zones maintaining a minimal distance between like-type cells. Cross-correlation analysis will determine the relationship between different types of cell. Evidence of exclusion zones in the autocorrelograms is suggestive of minimal-distance spacing rules operating between cells, and modeling studies will seek to define those rules by comparing simulations with real biological data. This project will therefore establish new tools for describing the spatial relationship between cells within a brain structure, specifically, the spacing rules that may govern their relative positioning. These tools will be made freely available to the scientific community for downloading from a website. They will provide the basis for future studies in which researchers can quantitate and model the spatial relationships between cells in the CNS in the process of exploring the biological mechanisms underlying intercellular spacing.